How to Use the Froged MCP in LlamaIndex

Q: How do I filter tools for my Froged and LlamaIndex setup?

Use the allowedtools filter during client initialization. This limits the agent's scope to only the specific tools you want it to access.

Index your Froged support data into LlamaIndex to ground your RAG applications in real-time customer intelligence.

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Connect Froged MCP to LlamaIndex

Create your Vinkius account to connect Froged to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Froged with LlamaIndex

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Claude

Perplexity

Turn support logs into a knowledge base

Feed the output from `list_support_conversations` directly into your vector store. You can now perform semantic searches across years of ticket history. This gives your agent a grounded memory of how you handled past issues. It stops the bot from guessing and starts providing answers based on actual past resolutions.

Query user behavior patterns

Fetch raw event data using `list_behavioral_events` to build a searchable index of user journeys. Your RAG app can then answer questions about what a user did before they reached out. It turns scattered event logs into a queryable timeline. Your agents will know exactly which features a user struggled with before they opened a chat.

Search help documentation instantly

Use `list_kb_articles` to sync your documentation into your index. This ensures your AI always has the latest product information when drafting support replies. Keep your help center and AI answers in sync. When you update a document in the dashboard, your index reflects it immediately.

Setup guide

Set up Froged MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Froged MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Froged tools.",
)
response = await agent.run("List recent Froged data")

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Common questions about Froged MCP in LlamaIndex

Indexing creates a searchable knowledge base from your live API data. It grounds your LLM in facts rather than letting it hallucinate answers.

Use the allowed_tools filter during client initialization. This limits the agent's scope to only the specific tools you want it to access.

The server uses ephemeral, zero-trust architecture. You define the ingestion boundaries, ensuring sensitive contact details are handled according to your security policy.

Yes, you can aggregate tools from different servers into a single tool list. It allows your agent to pull context from multiple sources simultaneously.

The client will throw an exception that your indexer can catch. You can then trigger a retry or flag the agent to wait until connectivity returns.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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